Nonlinear State Estimation

Nonlinear state estimation is the core research field at ISAS. It provides methods for determining estimates from noisy measurements of only indirectly accessible states. Typical examples are localization tasks or reconstruction of distributed phenomena. Other applications are described in the sections below. No satisfying generic approaches exist for nonlinear systems with stochastic disturbances today, as no closed form expressions with bounded complexity for recursive processing tasks are known. Since recursive processing is needed for most technical estimation problems, approximations are indispensable for real world applications.

The ISAS is developing new estimators belonging to the class of analytic filters. They allow closed form expressions with constant or adjustable complexity. The key idea is to employ different classes of probability densities which are able to approximate arbitrary probability densities with arbitrary precision. Such classes include Dirac-mixtures, Gaussian-Mixtures, hybrid and orthogonal densities. For these classes, closed form estimators can be derived. Due to the use of distance measures for approximating given densities, the quality of the estimator can be adjusted with respect to computational complexity

A further approach investigated is the use of non-stochastic uncertainties in the form of sets of probability densities. These methods allow the systematic incorporation of modelling and numeric uncertainties as well as inevitable approximations of estimators.

Intention Recognition

SFB 588 - Kitchen Setting.

Human-robot cooperation is governed by mutual estimation of intentions. Providing intention recognition capabilities to technical systems will enable these to communicate implicitly with their human users. The human-robot cooperation research project of the SFB 588 "Humanoid Robots - Learning and Cooperating Multimodal Robots" is guided by this insight.

Intention recognition at the ISAS is based on a stochastic modelling approach using hybrid dynamic bayes nets. Bayes nets are cascaded stochastic models and represent causal relations between intentions, actions and observations explicitely. The focus of our research is on a system theoretic treatment of non-linear variable dependancies and hybrid scenarios, which contain continous as well as discrete random variables. More...

Information Processing in Sensor-Actuator-Networks

Proceedings in miniaturization of microprocessors, sensors, and actuators allows to embed small low-cost sensor-actuator-nodes with wireless communication into the environment. Self-organizing networks consisting of a multitude of such nodes provide the opportunity for developing novel applications, e.g. realtime mapping of pollution concentrations in cities.

The model-based techniques for sensor-actuator-networks developed at ISAS allow to reconstruct and identify complex distributed phenomena (like the pollution concentration) using just a small number of measurements. By systematically treating the appearing uncertainties, the information gain of future measurements is predictable. Through this, optimal sensor scheduling with respect to high measurement accuracy and low needs in energy consumption and communication is possible.

A further aspect in reducing the communication and computation costs as well as in efficiently applying sensor-actuator-networks concerns with the decentralized execution, i.e., the distributed execution over all nodes of the developed algorithms. Here, the consideration of stochastic dependencies is very challenging. Robust methods that explicitly model these dependencies are currently investigated at ISAS.

Stochastic Model Predictive Control

In Model Predictive Control (MPC), not only the current system state of a technical system, e.g. a mobile robot, but also the future system behavior, which depends on the possible control inputs, is considered in the control.
This leads to a significant improvement in the quality of control.
The Stochastic Nonlinear Model Predictive Control (SNMPC) methods developed at ISAS are especially well-suited for nonlinear systems that are heavily noise-influenced as they explicitly consider these aspects in the control, which is accomplished by integrating the nonlinear state estimation techniques developed at ISAS. The results are applied to Networked Control Systems and multi-agent systems.

The practical evaluation of the ISAS SNMPC methods is done with a team of miniature walking robots. Here, the focus is especially on the motion control, the path planning and the resource scheduling.

Miniature Walking Robots

At ISAS, a team of miniature walking robots is been developed. The robots have six independent degrees of freedom, which allow them to move in a wide variety of motion patterns. These motion patterns comprise rotation, sideward and forward movement as well as a combination of all three of them.
The robots are radio controlled, have a high-performance rechargeable battery, and integrated computing resources for motion control.
For various experiments from the fields of nonlinear state estimation (e.g. cooperative position estimation), resource scheduling (e.g. measurement scheduling) as well as model predictive control (e.g. path planning), the robots are integrated into a test-environment.
In this test-environment, the robots’ poses are tracked by an overhead camera, which allows to simulate a wide variety of different sensors like, e.g. ultrasonic transducers for distance measurement

Motion Compensation of Beating Heart

In order to assist surgeons at minimal invasive interventions on the beating heart it would be helpful to develop a robotic surgery system, which synchronizes the instruments with the beating heart, so that their positions do not change relatively to the point of interest (POI). When the surgical robot takes care of the synchronization, the provided visual stabilization enables the presentation of the beating heart as if it was stationary and non-moving.

For synchronization of the surgical instruments with the POI and for the visual stabilization, the heart wall is regarded as a viscoelastic physical body. Its motion is described by a distributed parameter system mathematically formulated by a system of stochastic partial differential equations. By employing an element-free method for spatial discretization and a time integration method for temporal discretization, this system is converted in a state-space form. The internal parameters and states of the system are estimated by means of stochastic estimation approach, which processes discrete-time and discrete-space noisy camera measurements. As a result, the spatially and temporally distributed heart wall motion is reconstructed for arbitrary POIs even when no measurement information is available.

Multimodal Extended Range Telepresence

Mobile teleoperator consisting of a mobile platform equipped with a camera head

Telepresence aims at creating the impression of being present in a remote environment which is inaccessible by a human user. Such a remote environment can be real or virtual and is referred as target environment. The feeling of presence is achieved by visual and acoustic as well as haptic sensory information recorded from the target environment and presented to the user on an immersive display. In addition, omnidirectional walking along arbitrary distances is essential for telepresence in remote environments.
Therefore, in our extended range telepresence system the user can also walk freely in the target environment. For this purpose, the user's motion is tracked and transferred to a mobile teleoperator which replicates it. Without further processing of the motion information, the locomotion of the user in the target environment is restricted to the size of the user environment. Motion Compression, a nonlinear mapping between the user's and the mobile teleoperator's motion, allows exploration of large target environments even from small user environments.

Haptic information from the target environment is indispensable, first, to perceive objects and obstacles in the target environment more realistically and second, to provide the user with guidance information in the target environment. For this purpose, a large semi-mobile haptic interface that allows for simultaneous haptic interaction and wide-area motion was developed at ISAS.

Tracking of an Operator's Movements

For capturing the movement of persons, which is e.g. necessary in Large-Scale Telepresence, an acoustic tracking system is being developed at the ISAS. Multiple stationary loudspeakers simultaneously broadcast different signals, which are then captured by microphones attached to the persons and assigned to their respective sources. The posture of the person is estimated based on delay, loudspeaker positions and microphone positions. By using broadband signals, the system is robust against shadowing effects (i.e. people or furniture). A high level of accuracy is attained by using of a large number of microphones and speakers as well as modern signal processing and localization methods. With a newly developed localization method the acquisition rate at the same sampling frequency will be increased while retaining the same degree of precision as before. In this way, fast body movements can also be tracked reliably.

In cooperation with Prueftechnik Alignment Systems GmbH a measuring
system to calibrate multi-axes machine tools is beeing
developed. Objective is to replace conventional methods, which use
dial gauges and gauge steel, by a laser based method. The new system
will reduce the time needed for a machine calibration from a couple of
days to a few hours. On one hand, an instrument is being designed,
which will measure deviations within micrometers respectively
microdegrees. One the other hand research on algorithms is done, which
on the bases of a machine model determine out of a minimum of
measuring points the calibration parameters. Considering model and
measuring uncertainties as well as the prediction of optimal measuring
positions will ensure economic usability. These algorithms will be the
base for general concepts of sensor deployment planing.